Statistical Back Trajectory Analysis for Estimation of CO 2 Emission Source Regions

Statistical trajectory analysis has been widely used to identify potential source regions for chemically and radiatively important chemical species in the atmosphere. The most widely used method is a statistical source-receptor model developed by Stohl (1996), of which the underlying principle is that elevated concentrations at an observation site are proportionally related to both the average concentrations on a specific grid cell where the observed air mass has been passing over and the residence time staying over that grid cell. Thus, the method can compute a residence-time-weighted mean concentration for each grid cell by superimposing the back trajectory domain on the grid matrix. The concentration on a grid cell could be used as a proxy for potential source strength of corresponding species. This technical note describes the statistical trajectory approach and introduces its application to estimate potential source regions of CO2 enhancements observed at Korean Global Atmosphere Watch Observatory in Anmyeon-do. Back trajectories are calculated using HYSPLIT 4 model based on wind fields provided by NCEP GDAS. The identified CO2 potential source regions responsible for the pollution events observed at Anmyeon-do in 2010 were mainly Beijing area and the Northern China where Haerbin, Shenyang and Changchun mega cities are located. This is consistent with bottom-up emission information. In spite of inherent uncertainties of this method in estimating sharp spatial gradients within the vicinity of the emission hot spots, this study suggests that the statistical trajectory analysis can be a useful tool for identifying anthropogenic potential source regions for major GHGs.